Respiratory parameters such as respiratory rate and amplitude can provide vital information about a person's state of health. Diagnosis of several illnesses and disorders, for example sleep apnea, bradydysrhythmia, and/or bradycardia, can be based on the analysis of recordings of respiratory movements while the patient is asleep. Such recordings are often performed overnight in isolated hospital environments using polysomnography during which the patient is required to sleep under conditions of restricted motion while connected to numerous devices and electrodes. Furthermore, for continuous disorders such as sleep apnea, studies might be repeated several times to assess the effectiveness of treatment. The associated costs and discomfort can be high for the patient.
Patients at a risk of undergoing a sudden respiratory death risk have traditionally been monitored by electrocardiography (“ECG”). ECG monitoring can capture tachy dysrhythmias that can be a cause of preventable sudden respiratory death. Historically, it was believed that this was the most cost effective way to detect preventable sudden respiratory death of patients early. However, some patients experience bradydysrhythmia in the ten minutes prior to the calling of a code blue. Bradydysrhythmia is associated with hypoxia, which indicates that respiratory arrest, and not cardiac arrest, may be resulting in a respiratory arrest/death event in these patients. ECG monitoring may not detect the respiratory arrest early enough to allow for successful resuscitation. Respiratory monitoring technology exists. One approach currently available for monitoring respiratory arrest is pulse oximetry. Additionally, respiratory rate monitors, expired and/or transcutaneous CO2 monitoring, and air flow recording devices have also been used. Current respiratory rate and CO2 monitoring devices, however, are not always reliable nor do they produce reproducible data in the awake and active patient. CO2 monitoring can also be expensive and difficult to calibrate. Moreover, as with monitoring for sleep apnea, oximetry monitors rely on connecting the patient to numerous devices and/or electrodes.
As such, a non-invasive non-contact technique that offers low cost and reliable monitoring of respiratory movements is needed.
Ultra-wideband (UWB) technology offers the possibility of monitoring respiratory movements non-invasively and wirelessly. Indeed, UWB technology has been increasingly studied for ranging and imaging applications in medical environments. Compared with narrowband technologies, UWB offers the large bandwidth suitable for high-resolution ranging while operating in a low-power regime. UWB signals create no or minimal interference with other sensitive equipment in the surroundings, which can be of critical importance in medical environments. Although applications such as the monitoring of respiratory movements and diagnosis of the sleep apnea have been previously considered, they have been limited by several practical challenges. For example, earlier studies focus on estimating only vital signs, such as breathing and heart rates. Diagnosis of many illnesses and disorders, however, requires continuous monitoring of the respiratory amplitude to detect abnormalities in the breathing pattern. This requires accurate tracking of respiratory signals with high range resolution.
Monitoring via UWB can pose several challenges including multipath effects, low signal-to-noise ratios (SNR), and high sampling rate requirements. These challenges can be compounded by non-isolated and possibly time-variant environments. Some of these issues have been addressed, but no comprehensive scheme has been suggested that simultaneously deals with all of these issues. For example, earlier techniques have been based on time-of-arrival (TOA) methods that rely on accurate identification of the direct path component. However, in a multipath environment, a direct path may not exist or it may not be the strongest signal. Some schemes assume that the multipath environment is known and time-invariant, but this assumption is not practical in a non-isolated environment. Other schemes attempt to identify the direct path, which is a challenging task and adds additional complexity to the problem.
To improve the effective SNR, a technique has been suggested that utilizes the redundant information available in the sub-peaks of the received signal. See, for example, Lai et al., “Wireless Sensing of Human Respiratory Parameters by Low-Power Ultrawideband Impulse Radio Radar.” The effectiveness of this technique is, however, limited. A more widely used technique in typical UWB systems is to utilize multiple pulse transmissions to build a stronger received signal profile through averaging. Because of their large bandwidths, UWB systems require high sampling rates to recover information accurately from the received signals. A solution to this problem has not been previously offered. Further, the techniques rely on equipment such as the digital oscilloscope as their front-end hardware to achieve good tracking accuracy. Dependence on such complex hardware can be a major bottleneck in any efficient and cost-effective UWB solution.